
Then we adopt exactly the same global model as [1]} ganea2017deep which is already introduced in the Background section. Specifically, we adopt loopy belief propagation (LBP) to estimate the max-marginal probability \(\hat{g}_i(e|D)\)  and then combine it with the prior \(\hat{p}(e|m_i)\)  using a two-layer neural network to get the final score \(\rho _i(e)\)  for \(m_i\) .
\(\Phi (e,e^{\prime }) &= \frac{2}{n-1} \,\, {\mathbf {x}}_{e}^{\top }{\mathbf {C}}\, {\mathbf {x}}_{e^{\prime }} \\\hat{g}_i(e|D) &\approx \max _{\begin{array}{c}e_1,...,e_{i-1}\\e_{i+1},...,e_n\end{array}} g(e_1,...,e_n|D) \\\rho _i(e) &= f^{\prime }(\hat{g}_i(e|D), \hat{p}(e|m_i))\) 
